An Online Learning Model of Mobile User Preference Based on Context Quantification

In mobile network, the mobile user has the strict requirement for the performance of accessing the information. In order to provide the personalized service for mobile user timely and accurately, an online learning model of mobile user preference based on context quantification is proposed. In the model, a context quantification method is proposed, which can enhance the accuracy of learned mobile user preference; and the sliding window and the online extreme leaning machine (O-ELM) are introduced to realize the online learning. Firstly, it needs to judge whether the mobile user preference is affected by the context through analyzing the mobile user behaviors. Secondly, the context is quantified according to the context relevancy and the context similarity. And then, the sliding window is employed to select the samples that need to be learned when updating the mobile user preference. Finally, O-ELM is employed to learn the mobile user preference. The experimental results show that the proposed method surpasses the existing methods in the performance.

[1]  Vilas M. Thakare,et al.  Mobile Multimedia System Approach for Development of Mobile Applications & services with Data Visualization , 2013 .

[2]  Freddy Lécué,et al.  Using Context Similarity for Service Recommendation , 2010, 2010 IEEE Fourth International Conference on Semantic Computing.

[3]  Hai Yang,et al.  Improved Collaborative Filtering Recommendation Algorithm Based on Weighted Association Rules , 2013 .

[4]  Yuan Lan,et al.  Ensemble of online sequential extreme learning machine , 2009, Neurocomputing.

[5]  Javier López,et al.  Building trust from context similarity measures , 2014, Comput. Stand. Interfaces.

[6]  Hyunchul Ahn,et al.  A novel recommendation model of location-based advertising: Context-Aware Collaborative Filtering using GA approach , 2012, Expert Syst. Appl..

[7]  John Krogstie,et al.  Mobile Services Personalization Evaluation Model , 2013 .

[8]  Wenjie Pei,et al.  Mining Top-K Largest Tiles in a Data Stream , 2014, ECML/PKDD.

[9]  Yujie Zhang,et al.  Context-Aware Recommender Systems: Context-Aware Recommender Systems , 2012 .

[10]  Zhang Yujie.  Review on learning mobile user preferences for mobile network services , 2013 .

[11]  Elizabeth Papadopoulou,et al.  Adapting Pervasive Environments through Machine Learning and Dynamic Personalization , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications.

[12]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[13]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Evaggelia Pitoura,et al.  Adding Context to Preferences , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[15]  Martha Larson,et al.  TFMAP: optimizing MAP for top-n context-aware recommendation , 2012, SIGIR '12.

[16]  Chhavi Rana,et al.  An evolutionary clustering algorithm based on temporal features for dynamic recommender systems , 2014, Swarm Evol. Comput..

[17]  Dianhui Wang,et al.  Global Convergence of Online BP Training With Dynamic Learning Rate , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Zhang Yujie.  A Cognitive Psychology-Based Approach to User Preferences Elicitation for Mobile Network Services , 2011 .

[19]  Kenta Oku,et al.  Context-Aware SVM for Context-Dependent Information Recommendation , 2006, 7th International Conference on Mobile Data Management (MDM'06).

[20]  Shi Yan.  Adaptive Learning Approach of Contextual Mobile User Preferences , 2012 .